Four Scaling Axes for LLM Intelligence: Data, Weights, Reasoning, and RL
mariofilhoml · x · 2026-07-31
The author suggests that currently, LLM intelligence scales primarily across four core axes, where more quantity and diversity lead to better capabilities:
- Pretraining data: The scale and diversity of the training corpus.
- Total weights: The size of the model parameters.
- Reasoning tokens/budget: The compute allocated for inference, such as chain-of-thought tokens.
- RL environments: The richness of reinforcement learning environments.
The author speculates that the next potential axis might be the duration of RL environments, though this feels more like a property of the existing RL axis rather than a completely new dimension.
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